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Automated Financial ETL: Replaces manual spreadsheet downloads with a validated, logged pipeline that delivers clean currency rates at 7 AM daily.
Financial Data ETL Pipeline — Automated Currency Rates from API to Warehouse
Problem: Finance team spent 30 min every morning manually downloading exchange rate spreadsheets, reformatting them, and fixing null/broken values. One bad value could corrupt the whole report.
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An AI agent in an inventory system shouldn't place orders. It should notice things.
In TechFoundry IMS the agents do two jobs: read incoming purchase orders (messy PDFs, emails, the occasional photo) into structured records, and flag anomalies — a supplier's price jumping 30%, a quantity that doesn't match history, a duplicate PO with a different number.
The agent never changes stock levels. It writes a suggestion with its reasoning attached, and a person clicks approve. Same pattern as my review console, because it's the only pattern I've seen survive real operations.
If you run inventory or ops and you've been pitched "fully autonomous" — ask what happens on the day it's wrong. Case study: https://contra.com/p/eemeBeMW-tech-foundry-ims-ai-automated-inventory-and-order-operations
Super impressive work
Too many businesses try to build complex predictive AI models before their basic data pipelines are even functioning. You cannot skip the fundamentals.
The Data Hierarchy of Needs for scaling SMBs:
Level 1: Stable Collection (SQL) — Your database health and data pipelines. If your raw data is broken, everything built on top of it will fail.
Level 2: Data Governance (Python) — Enforcing uniform definitions, cleaning messy data, and ensuring total integrity.
Level 3: Automated BI (Power BI) — Moving away from manual spreadsheets to automated executive views and daily KPI tracking.
Level 4: Predictive Insights (AI / ML) — Churn models and forecasting. This only generates ROI if levels 1 through 3 are rock solid.
Stop wasting time on manual data entry and broken reports. If you are stuck at Level 1 or 2, I can help you scale up. Check out my Contra services to get your SQL pipelines automated and your Power BI dashboards built right the first time.
Amazing Information
From 7 Fragmented Data Sources to One Customer Identity Model
Problem. Seven disconnected Data Sources, no single customer identity, no traceability. Every business question turned into a reconciliation exercise before it could become a decision.
Action. Designed a Single Source of Truth with one customer identity model and explicit data stewardship. Ran the stack decision myself: evaluated AWS S3 + Athena against Redshift and BigQuery on three criteria — total cost at our data volume, existing team skillset, and time to first useful query. Chose S3 + Athena; the cost profile and the zero-ramp for the team beat the query performance advantage of the alternatives at our scale. Built the analytics layer on Power BI + Microsoft Fabric.
Result. An LLM-driven BI product adopted by 10 enterprise operators at daily and weekly cadence — the measure that mattered was recurring use, not dashboards delivered.
